Community-based collaboration recommendation to support mixed decision-making support

Yahia, Nesrine Ben; Saoud, Narjès Bellamine Ben; Ghezala, Henda Ben

doi:10.1080/12460125.2014.886495

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…The global gravity center g of the entire cloud is the cloud's weight divided by its cardinality. Finally,

d(x,y)

is the distance between x and y (which is generally the Euclidian distance) [5,39]. This sub‐metric concerns members' homogeneity with regard to the learners similarities and it considers node weights.…”

Section: Lifelong and Multirelational Community Detection Algorithmmentioning

confidence: 99%

“…To do so, we propose to use the Particle swarm optimization (PSO) algorithm [14] which is a stochastic optimization technique based on swarm [52]. Indeed, this choice is motivated by the rapidity and the fast convergence of this technique with regard to other optimization techniques such as the genetic algorithm, simulated annealing, ant colony algorithm [5]. Much more, it has given better results for NP-hard optimization and it is characterized by its simple implementation and its fast convergence [35].…”

Section: Hybrid Community Detection Algorithm At Each Time T Imentioning

confidence: 99%

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Lifelong and multirelational community detection to support social and collaborative e‐learning

Rebhi

Yahia

Bellamine

2022

Comp Applic In Engineering

Self Cite

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Learners' community choice has a crucial role in e‐learning effectiveness. Indeed, individual and structural factors (i.e., learners pre‐existing profiles and networks of interactions) significantly affect how learners develop a collaborative e‐learning environment. In this context, social network analysis, singularly community detection has been a good approach to improve collaborative environment through discovering pertinent learners' communities and new effective relations. However, with social media emergence, many real‐world networks, such as learners' networks, evolve the connections in multiple layers, where each layer represents a different type of relationship. This, combined with their continuous evolution over time, has brought new challenges to the field of community detection. Thus, this paper proposes a new configurable algorithm for detecting collaborative and lifelong communities within dynamic multirelational social learners' networks. To do so, this algorithm is based on a graph model to represent these different interactions as well as different learners' profiles and characteristics. Moreover, by using particle swarm optimization, it aims to optimize a configurable combined metric to detect the most relevant community appropriate to a given situation. Finally, it considers the temporal dimension to find the final lifelong community. By the end of this paper, experimental results using synthetic networks prove that the proposed algorithm achieves better results compared with other community detection algorithms. Therewith, experiments on a real e‐learning network show this algorithm's role in improving collaboration within learners' network.

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“…The global gravity center g of the entire cloud is the cloud's weight divided by its cardinality. Finally,

d(x,y)

Section: Lifelong and Multirelational Community Detection Algorithmmentioning

confidence: 99%

Section: Hybrid Community Detection Algorithm At Each Time T Imentioning

confidence: 99%

Lifelong and multirelational community detection to support social and collaborative e‐learning

Rebhi

Yahia

Bellamine

2022

Comp Applic In Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then, we represent the considered social network as a monoplex information graph (nodes represent researchers and each one has a weight which represents the similarity between this node profile and the problem holder profile; and edges represent collaborative relationship) in order to apply the combined community detection approach of [25]. Finally, the research laboratory is represented by a multiplex graph in order to apply the Generalized-modularity optimization approach of [27].…”

Section: Experimentation: Research Laboratory Case Studymentioning

confidence: 99%

“…This metric given by (8) is a function of two parameters: the context noted C and the time t i . Inspired from [25], the proposed metric Q T M IG is based on a weighted combination of two components that must be maximized simultaneously:…”

Section: Contextualized Community Detection Algorithmmentioning

confidence: 99%

Towards Contextualizing Community Detection in Dynamic Social Networks

Rebhi

Yahia

Saoud

et al. 2017

Modeling and Using Context

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Abstract. With the growing number of users and the huge amount of information in dynamic social networks, contextualizing community detection has been a challenging task. Thus, modeling these social networks is a key issue for the process of contextualized community detection. In this work, we propose a temporal multiplex information graphbased model to represent dynamic social networks: we consider simultaneously the social network dynamicity, its structure (different social connections) and various members' profiles so as to calculate similarities between "nodes" in each specific context. Finally a comparative study on a real social network shows the efficiency of our approach and illustrates practical uses.

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“…Recommending appropriate partners from a large amount of candidates becomes an interesting and challenging problem in the field of recommendation system [22,17,9,18,19,14].…”

Section: Introductionmentioning

confidence: 99%

A personalized recommendation system for NetEase dating site

et al. 2014

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With the popularity of internet, more and more people try to find friends or dating partners on online dating web sites. Recommending appropriate partners from a large amount of candidates becomes an interesting and challenging problem in the field of recommendation system. Various types of recommendation techniques (e.g., content based recommendation, collaborative filtering and association rule mining) have be proposed to tackle this problem. However most of them ignore the personalization concerns that they (1) mainly consider the hot users or frequent items, (2) cover only a portion of users especially ignoring the long tails, (3) and cannot deal with the cold start problem properly. In this paper, we present a regression based hybrid recommendation system that makes use of matching degree, fancy degree, activity, sincerity, popularity and enthusiasm, to recommend appropriate partners. The experimental evaluation of our recommendation system on a real dating web site shows our strategy is more effective and efficient than its previous version which follows the principle of giving higher priority to the recent active users.

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Community-based collaboration recommendation to support mixed decision-making support

Cited by 9 publications

References 31 publications

Lifelong and multirelational community detection to support social and collaborative e‐learning

Lifelong and multirelational community detection to support social and collaborative e‐learning

Towards Contextualizing Community Detection in Dynamic Social Networks

A personalized recommendation system for NetEase dating site

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